Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	13.0	37.004607
daal4py_KNeighborsClassifier	0.0	5.0	15.214882
KNeighborsClassifier_kd_tree	0.0	6.0	0.806752
daal4py_KNeighborsClassifier_kd_tree	0.0	1.0	49.451579
KMeans_tall	0.0	1.0	46.259200
daal4py_KMeans_tall	0.0	1.0	18.599999
KMeans_short	0.0	0.0	23.607057
daal4py_KMeans_short	0.0	0.0	14.179110
LogisticRegression	0.0	1.0	9.602413
daal4py_LogisticRegression	0.0	1.0	2.601084
Ridge	0.0	1.0	0.386971
daal4py_Ridge	0.0	0.0	24.476808
total	0.0	34.0	2.280701

KNeighborsClassifier vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	brute

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	throughput	latency	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier	fit	0.131	0.005	1000000	1000000	100	brute	-1	1	6.084	NaN	0.991	0.984	0.512	0.009	0.257	0.010	See
1	KNeighborsClassifier	predict	30.291	0.000	1000000	1000	100	brute	-1	1	0.000	0.030	0.991	0.984	3.843	0.026	7.882	0.053	See
2	KNeighborsClassifier	predict	0.200	0.018	1000000	1	100	brute	-1	1	0.004	0.000	0.991	0.984	0.101	0.003	1.970	0.185	See
3	KNeighborsClassifier	fit	0.129	0.008	1000000	1000000	100	brute	-1	5	6.183	NaN	0.991	0.984	0.518	0.015	0.250	0.018	See
4	KNeighborsClassifier	predict	38.494	0.000	1000000	1000	100	brute	-1	5	0.000	0.038	0.991	0.984	3.833	0.017	10.042	0.044	See
5	KNeighborsClassifier	predict	0.199	0.017	1000000	1	100	brute	-1	5	0.004	0.000	0.991	0.984	0.104	0.001	1.917	0.166	See
6	KNeighborsClassifier	fit	0.126	0.006	1000000	1000000	100	brute	-1	100	6.344	NaN	0.991	0.984	0.495	0.013	0.255	0.013	See
7	KNeighborsClassifier	predict	38.317	0.000	1000000	1000	100	brute	-1	100	0.000	0.038	0.991	0.984	3.883	0.029	9.868	0.074	See
8	KNeighborsClassifier	predict	0.204	0.011	1000000	1	100	brute	-1	100	0.004	0.000	0.991	0.984	0.099	0.002	2.053	0.122	See
9	KNeighborsClassifier	fit	0.141	0.005	1000000	1000000	100	brute	1	1	5.679	NaN	0.991	0.984	0.496	0.014	0.284	0.013	See
10	KNeighborsClassifier	predict	16.047	0.044	1000000	1000	100	brute	1	1	0.000	0.016	0.991	0.984	3.824	0.038	4.196	0.043	See
11	KNeighborsClassifier	predict	0.198	0.007	1000000	1	100	brute	1	1	0.004	0.000	0.991	0.984	0.103	0.001	1.928	0.072	See
12	KNeighborsClassifier	fit	0.125	0.003	1000000	1000000	100	brute	1	5	6.403	NaN	0.991	0.984	0.482	0.019	0.259	0.012	See
13	KNeighborsClassifier	predict	24.427	0.090	1000000	1000	100	brute	1	5	0.000	0.024	0.991	0.984	3.830	0.025	6.379	0.048	See
14	KNeighborsClassifier	predict	0.210	0.006	1000000	1	100	brute	1	5	0.004	0.000	0.991	0.984	0.102	0.002	2.063	0.071	See
15	KNeighborsClassifier	fit	0.131	0.005	1000000	1000000	100	brute	1	100	6.095	NaN	0.991	0.984	0.524	0.018	0.251	0.012	See
16	KNeighborsClassifier	predict	24.469	0.031	1000000	1000	100	brute	1	100	0.000	0.024	0.991	0.984	3.928	0.026	6.229	0.042	See
17	KNeighborsClassifier	predict	0.206	0.011	1000000	1	100	brute	1	100	0.004	0.000	0.991	0.984	0.106	0.004	1.935	0.120	See
18	KNeighborsClassifier	fit	0.052	0.002	1000000	1000000	2	brute	-1	1	0.305	NaN	0.991	0.984	0.100	0.004	0.523	0.029	See
19	KNeighborsClassifier	predict	24.877	0.143	1000000	1000	2	brute	-1	1	0.000	0.025	0.991	0.984	0.820	0.024	30.340	0.914	See
20	KNeighborsClassifier	predict	0.021	0.001	1000000	1	2	brute	-1	1	0.001	0.000	0.991	0.984	0.005	0.000	4.417	0.490	See
21	KNeighborsClassifier	fit	0.053	0.002	1000000	1000000	2	brute	-1	5	0.301	NaN	0.991	0.984	0.103	0.004	0.514	0.029	See
22	KNeighborsClassifier	predict	31.281	0.000	1000000	1000	2	brute	-1	5	0.000	0.031	0.991	0.984	0.810	0.009	38.619	0.442	See
23	KNeighborsClassifier	predict	0.034	0.003	1000000	1	2	brute	-1	5	0.000	0.000	0.991	0.984	0.004	0.000	7.849	1.125	See
24	KNeighborsClassifier	fit	0.052	0.001	1000000	1000000	2	brute	-1	100	0.309	NaN	0.991	0.984	0.100	0.003	0.517	0.020	See
25	KNeighborsClassifier	predict	31.406	0.000	1000000	1000	2	brute	-1	100	0.000	0.031	0.991	0.984	0.875	0.010	35.910	0.398	See
26	KNeighborsClassifier	predict	0.034	0.003	1000000	1	2	brute	-1	100	0.000	0.000	0.991	0.984	0.005	0.001	7.362	1.070	See
27	KNeighborsClassifier	fit	0.052	0.000	1000000	1000000	2	brute	1	1	0.311	NaN	0.991	0.984	0.104	0.002	0.497	0.010	See
28	KNeighborsClassifier	predict	10.669	0.030	1000000	1000	2	brute	1	1	0.000	0.011	0.991	0.984	0.807	0.016	13.221	0.262	See
29	KNeighborsClassifier	predict	0.015	0.001	1000000	1	2	brute	1	1	0.001	0.000	0.991	0.984	0.004	0.000	3.457	0.380	See
30	KNeighborsClassifier	fit	0.054	0.002	1000000	1000000	2	brute	1	5	0.296	NaN	0.991	0.984	0.100	0.006	0.539	0.039	See
31	KNeighborsClassifier	predict	18.543	0.059	1000000	1000	2	brute	1	5	0.000	0.019	0.991	0.984	0.808	0.011	22.938	0.334	See
32	KNeighborsClassifier	predict	0.029	0.001	1000000	1	2	brute	1	5	0.001	0.000	0.991	0.984	0.004	0.001	6.733	0.836	See
33	KNeighborsClassifier	fit	0.052	0.001	1000000	1000000	2	brute	1	100	0.305	NaN	0.991	0.984	0.099	0.006	0.530	0.034	See
34	KNeighborsClassifier	predict	18.849	0.100	1000000	1000	2	brute	1	100	0.000	0.019	0.991	0.984	0.875	0.010	21.545	0.269	See
35	KNeighborsClassifier	predict	0.030	0.001	1000000	1	2	brute	1	100	0.001	0.000	0.991	0.984	0.005	0.001	6.321	0.990	See

KNeighborsClassifier_kd_tree vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	kd_tree

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	throughput	latency	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier_kd_tree	fit	2.796	0.037	1000000	1000000	10	kd_tree	-1	1	0.029	NaN	0.983	0.982	0.723	0.012	3.865	0.081	See
1	KNeighborsClassifier_kd_tree	predict	0.436	0.009	1000000	1000	10	kd_tree	-1	1	0.000	0.000	0.983	0.982	0.126	0.004	3.454	0.142	See
2	KNeighborsClassifier_kd_tree	predict	0.007	0.003	1000000	1	10	kd_tree	-1	1	0.011	0.000	0.983	0.982	0.000	0.000	29.455	21.175	See
3	KNeighborsClassifier_kd_tree	fit	2.813	0.034	1000000	1000000	10	kd_tree	-1	5	0.028	NaN	0.983	0.982	0.760	0.011	3.700	0.068	See
4	KNeighborsClassifier_kd_tree	predict	0.882	0.013	1000000	1000	10	kd_tree	-1	5	0.000	0.001	0.983	0.982	0.228	0.005	3.865	0.105	See
5	KNeighborsClassifier_kd_tree	predict	0.003	0.000	1000000	1	10	kd_tree	-1	5	0.025	0.000	0.983	0.982	0.001	0.001	4.025	4.172	See
6	KNeighborsClassifier_kd_tree	fit	2.738	0.077	1000000	1000000	10	kd_tree	-1	100	0.029	NaN	0.983	0.982	0.716	0.010	3.823	0.120	See
7	KNeighborsClassifier_kd_tree	predict	2.802	0.035	1000000	1000	10	kd_tree	-1	100	0.000	0.003	0.983	0.982	0.675	0.008	4.153	0.070	See
8	KNeighborsClassifier_kd_tree	predict	0.006	0.001	1000000	1	10	kd_tree	-1	100	0.012	0.000	0.983	0.982	0.001	0.000	10.244	4.270	See
9	KNeighborsClassifier_kd_tree	fit	2.767	0.068	1000000	1000000	10	kd_tree	1	1	0.029	NaN	0.983	0.982	0.760	0.010	3.641	0.101	See
10	KNeighborsClassifier_kd_tree	predict	0.759	0.012	1000000	1000	10	kd_tree	1	1	0.000	0.001	0.983	0.982	0.133	0.010	5.724	0.456	See
11	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	kd_tree	1	1	0.074	0.000	0.983	0.982	0.000	0.000	3.786	1.802	See
12	KNeighborsClassifier_kd_tree	fit	2.723	0.035	1000000	1000000	10	kd_tree	1	5	0.029	NaN	0.983	0.982	0.730	0.012	3.732	0.076	See
13	KNeighborsClassifier_kd_tree	predict	1.507	0.035	1000000	1000	10	kd_tree	1	5	0.000	0.002	0.983	0.982	0.224	0.004	6.725	0.191	See
14	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	kd_tree	1	5	0.060	0.000	0.983	0.982	0.000	0.000	4.424	2.164	See
15	KNeighborsClassifier_kd_tree	fit	2.797	0.064	1000000	1000000	10	kd_tree	1	100	0.029	NaN	0.983	0.982	0.771	0.026	3.628	0.150	See
16	KNeighborsClassifier_kd_tree	predict	4.962	0.039	1000000	1000	10	kd_tree	1	100	0.000	0.005	0.983	0.982	0.677	0.010	7.330	0.125	See
17	KNeighborsClassifier_kd_tree	predict	0.004	0.001	1000000	1	10	kd_tree	1	100	0.018	0.000	0.983	0.982	0.001	0.000	6.689	3.237	See
18	KNeighborsClassifier_kd_tree	fit	0.745	0.026	1000000	1000000	2	kd_tree	-1	1	0.021	NaN	0.983	0.982	0.496	0.014	1.502	0.068	See
19	KNeighborsClassifier_kd_tree	predict	0.039	0.009	1000000	1000	2	kd_tree	-1	1	0.000	0.000	0.983	0.982	0.001	0.000	47.717	16.541	See
20	KNeighborsClassifier_kd_tree	predict	0.003	0.000	1000000	1	2	kd_tree	-1	1	0.006	0.000	0.983	0.982	0.000	0.000	14.983	6.996	See
21	KNeighborsClassifier_kd_tree	fit	0.746	0.023	1000000	1000000	2	kd_tree	-1	5	0.021	NaN	0.983	0.982	0.502	0.010	1.484	0.056	See
22	KNeighborsClassifier_kd_tree	predict	0.037	0.002	1000000	1000	2	kd_tree	-1	5	0.000	0.000	0.983	0.982	0.001	0.000	26.928	6.880	See
23	KNeighborsClassifier_kd_tree	predict	0.003	0.000	1000000	1	2	kd_tree	-1	5	0.006	0.000	0.983	0.982	0.000	0.000	16.661	7.951	See
24	KNeighborsClassifier_kd_tree	fit	0.751	0.023	1000000	1000000	2	kd_tree	-1	100	0.021	NaN	0.983	0.982	0.498	0.013	1.507	0.060	See
25	KNeighborsClassifier_kd_tree	predict	0.061	0.006	1000000	1000	2	kd_tree	-1	100	0.000	0.000	0.983	0.982	0.007	0.001	8.563	1.096	See
26	KNeighborsClassifier_kd_tree	predict	0.003	0.000	1000000	1	2	kd_tree	-1	100	0.006	0.000	0.983	0.982	0.000	0.000	13.887	6.661	See
27	KNeighborsClassifier_kd_tree	fit	0.769	0.018	1000000	1000000	2	kd_tree	1	1	0.021	NaN	0.983	0.982	0.503	0.006	1.528	0.041	See
28	KNeighborsClassifier_kd_tree	predict	0.031	0.001	1000000	1000	2	kd_tree	1	1	0.001	0.000	0.983	0.982	0.001	0.000	37.648	11.143	See
29	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	1	0.020	0.000	0.983	0.982	0.000	0.000	4.860	2.434	See
30	KNeighborsClassifier_kd_tree	fit	0.767	0.019	1000000	1000000	2	kd_tree	1	5	0.021	NaN	0.983	0.982	0.517	0.011	1.484	0.049	See
31	KNeighborsClassifier_kd_tree	predict	0.034	0.001	1000000	1000	2	kd_tree	1	5	0.000	0.000	0.983	0.982	0.001	0.000	28.893	5.399	See
32	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	5	0.020	0.000	0.983	0.982	0.000	0.000	4.661	2.394	See
33	KNeighborsClassifier_kd_tree	fit	0.767	0.015	1000000	1000000	2	kd_tree	1	100	0.021	NaN	0.983	0.982	0.517	0.007	1.484	0.035	See
34	KNeighborsClassifier_kd_tree	predict	0.063	0.004	1000000	1000	2	kd_tree	1	100	0.000	0.000	0.983	0.982	0.007	0.001	8.571	1.012	See
35	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	100	0.018	0.000	0.983	0.982	0.000	0.000	5.068	2.677	See

KMeans_tall vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	throughput	latency	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_tall	fit	0.616	0.014	1000000	1000000	2	full	k-means++	30	3	1	0.0	30	0.780	NaN	0.002	30	0.002	0.319	0.014	1.927	0.094	See
1	KMeans_tall	predict	0.002	0.000	1000000	1000	2	full	k-means++	30	3	1	0.0	30	0.010	0.0	0.002	30	0.002	0.000	0.000	7.738	3.694	See
2	KMeans_tall	predict	0.002	0.000	1000000	1	2	full	k-means++	30	3	1	0.0	30	0.010	0.0	0.002	30	0.002	0.000	0.000	6.641	3.817	See
3	KMeans_tall	fit	0.522	0.014	1000000	1000000	2	full	random	30	3	1	0.0	30	0.920	NaN	0.002	30	0.002	0.272	0.006	1.917	0.067	See
4	KMeans_tall	predict	0.002	0.000	1000000	1000	2	full	random	30	3	1	0.0	30	0.010	0.0	0.002	30	0.002	0.000	0.000	7.409	3.166	See
5	KMeans_tall	predict	0.002	0.000	1000000	1	2	full	random	30	3	1	0.0	30	0.010	0.0	0.002	30	0.002	0.000	0.000	9.124	5.033	See
6	KMeans_tall	fit	7.132	0.082	1000000	1000000	100	full	k-means++	30	3	1	0.0	30	3.365	NaN	0.002	30	0.002	3.980	0.054	1.792	0.032	See
7	KMeans_tall	predict	0.002	0.000	1000000	1000	100	full	k-means++	30	3	1	0.0	30	0.453	0.0	0.002	30	0.002	0.000	0.000	5.710	1.883	See
8	KMeans_tall	predict	0.002	0.000	1000000	1	100	full	k-means++	30	3	1	0.0	30	0.491	0.0	0.002	30	0.002	0.000	0.000	8.087	4.364	See
9	KMeans_tall	fit	6.563	0.073	1000000	1000000	100	full	random	30	3	1	0.0	30	3.657	NaN	0.002	30	0.002	3.754	0.050	1.749	0.030	See
10	KMeans_tall	predict	0.002	0.000	1000000	1000	100	full	random	30	3	1	0.0	30	0.436	0.0	0.002	30	0.002	0.000	0.000	6.281	2.296	See
11	KMeans_tall	predict	0.002	0.000	1000000	1	100	full	random	30	3	1	0.0	30	0.523	0.0	0.002	30	0.002	0.000	0.000	8.735	3.987	See

KMeans_short vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	30
n_init	1
tol	0.0

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	throughput	latency	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_short	fit	0.322	0.016	10000	10000	2	full	k-means++	30	300	1	0.0	28	0.014	NaN	0.005	30	0.003	0.149	0.006	2.159	0.136	See
1	KMeans_short	predict	0.002	0.000	10000	1000	2	full	k-means++	30	300	1	0.0	28	0.008	0.0	0.005	30	0.003	0.001	0.000	3.092	0.662	See
2	KMeans_short	predict	0.002	0.000	10000	1	2	full	k-means++	30	300	1	0.0	28	0.011	0.0	0.005	30	0.003	0.000	0.000	8.039	3.640	See
3	KMeans_short	fit	0.143	0.012	10000	10000	2	full	random	30	300	1	0.0	30	0.034	NaN	0.005	30	0.003	0.069	0.003	2.063	0.191	See
4	KMeans_short	predict	0.002	0.000	10000	1000	2	full	random	30	300	1	0.0	30	0.007	0.0	0.005	30	0.003	0.001	0.000	3.234	0.750	See
5	KMeans_short	predict	0.002	0.000	10000	1	2	full	random	30	300	1	0.0	30	0.011	0.0	0.005	30	0.003	0.000	0.000	7.000	4.182	See
6	KMeans_short	fit	1.058	0.066	10000	10000	100	full	k-means++	30	300	1	0.0	17	0.129	NaN	0.005	19	0.003	0.609	0.048	1.738	0.174	See
7	KMeans_short	predict	0.003	0.000	10000	1000	100	full	k-means++	30	300	1	0.0	17	0.261	0.0	0.005	19	0.003	0.002	0.000	1.905	0.430	See
8	KMeans_short	predict	0.002	0.000	10000	1	100	full	k-means++	30	300	1	0.0	17	0.465	0.0	0.005	19	0.003	0.000	0.000	8.244	4.093	See
9	KMeans_short	fit	0.296	0.033	10000	10000	100	full	random	30	300	1	0.0	21	0.568	NaN	0.005	26	0.003	0.331	0.032	0.894	0.133	See
10	KMeans_short	predict	0.003	0.000	10000	1000	100	full	random	30	300	1	0.0	21	0.262	0.0	0.005	26	0.003	0.002	0.000	1.977	0.382	See
11	KMeans_short	predict	0.002	0.001	10000	1	100	full	random	30	300	1	0.0	21	0.378	0.0	0.005	26	0.003	0.000	0.000	10.061	8.111	See

LogisticRegression vs daal4py

All estimators share the following hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	n_iter	throughput	latency	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	LogisticRegression	fit	15.449	0.046	1000000	1000000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	[-0.07637218]	NaN	0.26	15.528	0.187	0.995	0.012	See
1	LogisticRegression	predict	0.000	0.000	1000000	1000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	2.1925497155176576	0.0	0.26	0.000	0.000	0.847	0.375	See
2	LogisticRegression	predict	0.000	0.000	1000000	1	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	8.225332995412497	0.0	0.26	0.000	0.000	0.276	0.286	See
3	LogisticRegression	fit	1.186	0.089	1000	1000	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[26]	[1.75386157]	NaN	0.26	1.289	0.023	0.920	0.071	See
4	LogisticRegression	predict	0.002	0.000	1000	100	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[26]	3.4158805052975714	0.0	0.26	0.004	0.000	0.568	0.089	See
5	LogisticRegression	predict	0.000	0.000	1000	1	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[26]	61.246035288228306	0.0	0.26	0.001	0.000	0.154	0.079	See

Ridge vs daal4py

All estimators share the following hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	deprecated
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	n_iter	throughput	latency	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	Ridge	fit	2.734	0.055	100000	100000	1000	1.0	True	True	NaN	deprecated	NaN	auto	0.001	NaN	0.293	NaN	1.0	1.580	0.023	1.730	0.043	See
1	Ridge	predict	0.001	0.000	100000	1000	1000	1.0	True	True	NaN	deprecated	NaN	auto	0.001	NaN	10.164	0.0	1.0	0.002	0.002	0.429	0.431	See
2	Ridge	predict	0.000	0.000	100000	1	1000	1.0	True	True	NaN	deprecated	NaN	auto	0.001	NaN	83.814	0.0	1.0	0.000	0.000	0.674	0.486	See
3	Ridge	fit	1.228	0.026	1000000	1000000	100	1.0	True	True	NaN	deprecated	NaN	auto	0.001	NaN	0.651	NaN	1.0	0.340	0.010	3.611	0.131	See
4	Ridge	predict	0.000	0.000	1000000	1000	100	1.0	True	True	NaN	deprecated	NaN	auto	0.001	NaN	4.772	0.0	1.0	0.000	0.000	0.690	0.417	See
5	Ridge	predict	0.000	0.000	1000000	1	100	1.0	True	True	NaN	deprecated	NaN	auto	0.001	NaN	9.168	0.0	1.0	0.000	0.000	0.619	0.464	See

Environment information

{
  "system_info": {
    "python": "3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1046-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.1",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.2",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": null,
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}